Efficient double-quenching of electrochemiluminescence from CdS:Eu QDs by hemin-graphene-Au nanorods ternary composite for ultrasensitive immunoassay

A novel ternary composite of hemin-graphene-Au nanorods (H-RGO-Au NRs) with high electrocatalytic activity was synthesized by a simple method. And this ternary composite was firstly used in construction of electrochemiluminescence (ECL) immunosensor due to its double-quenching effect of quantum dots (QDs). Based on the high electrocatalytic activity of ternary complexes for the reduction of H(2)O(2) which acted as the coreactant of QDs-based ECL, as a result, the ECL intensity of QDs decreased. Besides, due to the ECL resonance energy transfer (ECL-RET) strategy between the large amount of Au nanorods (Au NRs) on the ternary composite surface and the CdS:Eu QDs, the ECL intensity of QDs was further quenched. Based on the double-quenching effect, a novel ultrasensitive ECL immunoassay method for detection of carcinoembryonic antigen (CEA) which is used as a model biomarker analyte was proposed. The designed immunoassay method showed a linear range from 0.01 pg mL(−1) to 1.0 ng mL(−1) with a detection limit of 0.01 pg mL(−1). The method showing low detection limit, good stability and acceptable fabrication reproducibility, provided a new approach for ECL immunoassay sensing and significant prospect for practical application

Equipments for Crop Protection:Standardization Development in China

 The history of standardization for crop protection equipments was reviewed to analyze the trends of standards preparation in this paper. The currently active standards were firstly reviewed by their attributes to present the general state of art. The trends of standard preparation, through which the overall development of crop protection equipments are reflected, were interpreted by descriptive items. Finally the future development was predicted as suggestions for decision-making in policy constitution

Modeling Price Volatility based on a Genetic Programming Approach

Business profitability is highly dependent on risk management strategies to hedge future cash flow uncertainty. Commodity price shocks and fluctuations are key risks for companies with global supply chains. The purpose of this paper is to show how Artificial Intelligence (AI) techniques can be used to model the volatility of commodity prices. More specifically we introduce a new model – LIQ-GARCH - that uses Genetic Programming to forecast volatility. The newly generated model is then used to forecast the volatility of the following three indexes: the Commodity Research Bureau (CRB) index, the West Texas Intermediate (WTI) oil futures prices and the Baltic Dry Index (BDI). The empirical model performance tests show that the newly generated model in this paper is considerably more accurate than the traditional GARCH model. As a result, this model can help businesses to design optimal risk management strategies and to hedge themselves against price uncertainty

DETC2003/CIE-48270 A FRAMEWORK FOR INTERNET BASED PRODUCT INFORMATION SHARING AND VISUALIZATION

ABSTRACT Internet based product information sharing and visualization is the foundation for collaborative product design and manufacturing. This paper presents a Web based framework with a STEP based product data master model and VRML based visualization techniques for visualizing and sharing product information among designers, production engineers and managers, purchasing and marketing staff, suppliers, and customers. A prototype software environment is implemented to validate the proposed framework and related technologies

MobileInst: Video Instance Segmentation on the Mobile

Video instance segmentation on mobile devices is an important yet very challenging edge AI problem. It mainly suffers from (1) heavy computation and memory costs for frame-by-frame pixel-level instance perception and (2) complicated heuristics for tracking objects. To address those issues, we present MobileInst, a lightweight and mobile-friendly framework for video instance segmentation on mobile devices. Firstly, MobileInst adopts a mobile vision transformer to extract multi-level semantic features and presents an efficient query-based dual-transformer instance decoder for mask kernels and a semantic-enhanced mask decoder to generate instance segmentation per frame. Secondly, MobileInst exploits simple yet effective kernel reuse and kernel association to track objects for video instance segmentation. Further, we propose temporal query passing to enhance the tracking ability for kernels. We conduct experiments on COCO and YouTube-VIS datasets to demonstrate the superiority of MobileInst and evaluate the inference latency on one single CPU core of Snapdragon 778G Mobile Platform, without other methods of acceleration. On the COCO dataset, MobileInst achieves 31.2 mask AP and 433 ms on the mobile CPU, which reduces the latency by 50% compared to the previous SOTA. For video instance segmentation, MobileInst achieves 35.0 AP on YouTube-VIS 2019 and 30.1 AP on YouTube-VIS 2021. Code will be available to facilitate real-world applications and future research.Comment: Accepted by AAAI 2024 Main Track; Code will be release

TouchStone: Evaluating Vision-Language Models by Language Models

Large vision-language models (LVLMs) have recently witnessed rapid advancements, exhibiting a remarkable capacity for perceiving, understanding, and processing visual information by connecting visual receptor with large language models (LLMs). However, current assessments mainly focus on recognizing and reasoning abilities, lacking direct evaluation of conversational skills and neglecting visual storytelling abilities. In this paper, we propose an evaluation method that uses strong LLMs as judges to comprehensively evaluate the various abilities of LVLMs. Firstly, we construct a comprehensive visual dialogue dataset TouchStone, consisting of open-world images and questions, covering five major categories of abilities and 27 subtasks. This dataset not only covers fundamental recognition and comprehension but also extends to literary creation. Secondly, by integrating detailed image annotations we effectively transform the multimodal input content into a form understandable by LLMs. This enables us to employ advanced LLMs for directly evaluating the quality of the multimodal dialogue without requiring human intervention. Through validation, we demonstrate that powerful LVLMs, such as GPT-4, can effectively score dialogue quality by leveraging their textual capabilities alone, aligning with human preferences. We hope our work can serve as a touchstone for LVLMs' evaluation and pave the way for building stronger LVLMs. The evaluation code is available at https://github.com/OFA-Sys/TouchStone.Comment: https://github.com/OFA-Sys/TouchSton

Single and combined use of the platelet-lymphocyte ratio, neutrophil-lymphocyte ratio, and systemic immune-inflammation index in gastric cancer diagnosis

IntroductionThe platelet-lymphocyte ratio (PLR), neutrophil-lymphocyte ratio (NLR), and systemic immune-inflammation index (SII) are markers for systemic inflammatory responses and have been shown by numerous studies to correlate with the prognosis of gastric cancer (GC). However, the diagnostic value of these three markers in GC is unclear, and no research has examined them in combination. In this study, we investigated the value of the PLR, NLR, and SII individually or in combination for GC diagnosis and elucidated the connection of these three markers with GC patients’ clinicopathological features.MethodsThis retrospective study was conducted on 125 patients diagnosed with GC and 125 healthy individuals, whose peripheral blood samples were obtained for analysis. The preoperative PLR, NLR, and SII values were subsequently calculated.ResultsThe results suggest that the PLR, NLR, and SII values of the GC group were considerably higher than those of the healthy group (all P ≤ 0.001); moreover, all three parameters were notably higher in early GC patients (stage I/II) than in the healthy population. The diagnostic value of each index for GC was analyzed using receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) calculation. The diagnostic efficacy of the SII alone (AUC: 0.831; 95% confidence interval [CI], 0.777–0.885) was expressively better than those of the NLR (AUC: 0.821; 95% CI: 0.769–0.873, P = 0.017) and PLR (AUC: 0.783; 95% CI: 0.726–0.840; P = 0.020). The AUC value of the combination of the PLR, NLR, and SII (AUC: 0.843; 95% CI: 0.791–0.885) was significantly higher than that of the combination of the SII and NLR (0.837, 95% CI: 0.785–0.880, P≤0.05), PLR (P = 0.020), NLR (P = 0.017), or SII alone (P ≤ 0.001). The optimal cut-off values were determined for the PLR, NLR, and SII using ROC analysis (SII: 438.7; NLR: 2.1; PLR: 139.5). Additionally, the PLR, NLR, and SII values were all meaningfully connected with the tumor size, TNM stage, lymph node metastasis, and serosa invasion (all P ≤ 0.05). Elevated levels of the NLR and SII were linked to distant metastasis (all P ≤ 0.001).DiscussionThese data suggest that the preoperative PLR, NLR, and SII could thus be utilized as diagnostic markers for GC or even early GC. Among these three indicators, the SII had the best diagnostic efficacy for GC, and the combination of the three could further improve diagnostic efficiency

Ultrasonographic and clinicopathological features of pelvic yolk sac tumors in women: a single-center retrospective analysis

ObjectivesYolk sac tumors (YSTs) are rare and highly malignant ovarian malignancies that have a very poor prognosis. The aim of this study is to delineate the ultrasound and clinicopathological features of female pelvic YSTs to better understand the disease.MethodsThis study was a retrospective analysis of the clinicopathological and ultrasound imaging data from 16 YST patients who received treatment at our hospital between January 2012 and August 2023. Then, the ultrasound imaging characteristics were compared with pathological findings.ResultsAmong the 16 patients, various degrees of serum AFP increase were observed, and CA125 levels increased in 58.33% (7 out of 12) of patients. Thirteen patients (81.25%) had tumors located in ovary, two patients (12.5%) had tumors located in the sacrococcygeal region, and one patient (6.25%) had tumors located in the mesentery. Pathologically, nine patients presented with simple yolk sac tumors and seven with mixed germ cell tumors. According to the ultrasound manifestations, YST lesions can be classified into three types. (1) the cystic type, was diagnosed in two patients who presented with a large cystic mass with regular morphology and clear boundary and dense liquid within the cyst; and (2) the cystic-solid mixed type, was diagnosed in 4 patients. On 2D ultrasound, the lesions showed a cystic-solid mixed echo, and color Doppler showed a rich blood flow signal in the solid region and cystic separation. made up of four cases. (3) In ten patients with the solid type, 2D ultrasound showed solid uniform echoes with clear boundaries. The “fissure sign” was observed in the lesion. Color Doppler displayed rich blood flow in the solid part, and PW showed low to moderate resistance index of artery (RI:0.21–0.63). On contrast-enhanced ultrasound (CEUS), rapid and high enhancement in the solid part and cystic separation was observed in 2 patients.ConclusionsCombining ultrasound features with clinical information and tumor markers provides reliable clues for the diagnosis of YST. The application of two-dimensional ultrasound and CEUS combined with patient tumor marker levels can provide a robust reference for determining the necessity of fertility-preserving surgery and postoperative chemotherapy, which can improve clinical decision-making and patient consultation